AIETDS 2027

The AIETDS is a serial conference focusing on experimental, theoretical and applied innovations across Artificial Intelligence, Education Technology and Data Science. The conference strives to build an inclusive interdisciplinary platform, bringing together academic researchers, scholars and industrial specialists engaged in fundamental research, applied science and engineering technology to exchange cutting-edge discoveries and valuable insights.

We sincerely invite experts, scholars and industry practitioners from universities and research institutions worldwide to submit papers and join our academic communications. Topics of interest include, but are not limited to:

🔹 Track 1: AI in Education Methodology • Generative Content Trustworthiness • Multilingual LLM Fine-tuning for Education • Cross-disciplinary Multimodal Fusion • Reinforcement Learning for Cognitive Modeling • Personalized Prompt Engineering • Few-shot Scenario Adaptation • Lightweight AI Deployment • Educational Agent Collaboration • Autonomous Reasoning of Agents • Generative Assessment and Feedback • LLM-driven Personalized Learning Paths • Full-lifecycle Management of Educational LLMs • Human-AI Knowledge Co-creation • Multimodal Cognitive LLMs	🔹 Track 2: Educational Data Science and Privacy-Preserving Computing • Causal Attribution in Learning • Educational Knowledge Graph Reasoning • Multimodal Learning Analytics Pipeline • Temporal and Streaming Data Modeling • Privacy-preserving Educational Mining • Compliance-oriented Data Governance • Visual Analytics for Education • Real-time Classroom Engagement Intervention • Multimodal Interaction in Synchronous Classrooms • BCI-driven Learning State Monitoring • Open Benchmarks and Reproducibility

🔹 Track 3: Intelligent Educational Systems • Neuro-inspired Adaptive Learning • Metaverse Interoperability for Education • Digital Twin Teaching Environments • Adversarial Robustness of Educational AI • Edge Computing for Special Education • Secure Offloading in Educational AI • Embodied Intelligence for Education • Lightweight VR/AR Teaching Tools • Educational AI Deployment and Operation • Embodied Agent Interaction • Digital Twin Learning Environments • Domain-specific LLMs for Vocational Education • BCI-based Instructional Intervention	🔹 Track 4: Ethics and Governance of AI in Education • Bias Detection and Fairness Enhancement • Transparency and Accountability Algorithms • Responsible AI by Design • Value Alignment of Educational LLMs • Continuous Ethical Impact Assessment • Ethical Audit and Access Certification • AI-dependency and Cognitive Decline Intervention • Dynamic Task-responsibility Allocation • Teacher AI Competency Assessment • SDG4 Alignment and Impact Quantification • Data and Model Security for Educational AI • AI-based Cyberbullying Detection and Safeguarding • Cognitive Security in Human-AI Interaction • Neuro-data Ethics and Privacy in BCI • Cross-cultural AI Governance Comparison

🔹 Track 5: Data Mining and Machine Learning • Large-scale Pattern Discovery • Novel Deep Representation Architectures • Few-shot, Zero-shot and Transfer Learning • Explainable and Fair Data Science • Reinforcement Learning and Decision Intelligence • Anomaly and Change-point Detection • Graph Neural Networks and Knowledge Reasoning • Causal and Counterfactual Inference	🔹 Track 6: Data Engineering and Visualization • Data Quality and Preprocessing • Distributed Data Warehouses and Lakes • Real-time Stream Processing • Interactive Visual Analytics • Privacy-preserving Computation • Data Governance and Metadata Management • Synthetic Data in Educational Research • Synthetic Data Generation and Evaluation • Human-in-the-loop Data Exploration • Digital-physical Fusion and Spatial Computing